Action Recognition Unrestricted by Location and Viewpoint Variation

Action recognition is a popular research topic in computer vision. So far most of proposed algorithms are under assumptions of fixed location and viewpoint of the subject, which are usually not valid in practical environment where the subject might roam in the field. To address the difficulties of action recognition tolerating location and view angle variation, we propose an "Adapted Envelop Shape" based approach, which is a posture invariance representation and extendible to multi-camera environment. Further Adapted Envelop Shape is used as input vector for Hidden Markov Model to train and recognize actions. Our method has following desirable properties: 1) Exact camera calibration is not needed. 2) Action recognition is view point and location invariant. 3) Automatic switch of cameras according to human location makes visible area more wide. 4) Partially occlusion or out of sight of human body is tolerable. Experimental results also demonstrate the effectiveness of our method.